In recent years, video has become the dominant resource of information on the Web, where the text within the video usually carries significant semantic information. Video text extraction and recognition plays an essential role in web multimedia understanding and retrieval for big visual data analytics and applications. To deal with challenging background and embedding noises, most conventional approaches usually tend to design sophisticated pre-processing and post-progressing steps before and after text detection. In this paper, Yang et al. present a simple yet powerful pipeline that directly and uniformly detects Chinese text lines for embedded captions from web videos.

Aims and scope

Big Data Analytics is a multi-disciplinary open access, peer-reviewed journal, which welcomes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of big data science analytics. Spanning the life sciences, social sciences, engineering, physical and mathematical sciences, Big Data Analytics aims to provide a platform for the dissemination of research, current practices, and future trends in the emerging discipline of big data analytics.

Big Data Analytics is pleased to announce a call for papers for a new article collection of original, unpublished, and novel in-depth research that makes significant methodological or application contributions to the field of visualization, interpretation and descriptive big data science. Read more and all the current calls for papers at the link above.

In this Q&A, Editor-in-Chief Amir Hussain tells us more about Big Data Analytics.

Amir Hussain, Editor-in-Chief

Amir Hussainobtained his BEng and PhD from the University of Strathclyde in Glasgow. Following a Research Fellowship at the University of Paisley and a research Lectureship at the University of Dundee, he joined the University of Stirling in 2000, where he is currently Professor of Computing Science and founding Director of the Cognitive Big Data Informatics (CogBID) Laboratory. His research interests are multi-disciplinary with a focus on brain-inspired, multi-modal cognitive big data technology for solving complex real-world problems. He has authored over 300 publications (including over a dozen books and over 100 journal papers), conducted and led collaborative research with industry, partnered in major European and international research programs, and supervised more than 30 PhDs.

In addition to his role as Editor-in-Chief of Big Data Analytics, Amir is also founding Chief Editor of the Springer journal Cognitive Computation, and Associate Editor for a number of other leading journals. He has served as an invited speaker and organizing committee co-chair for over 50 top international conferences and workshops. He has served as the founding Publications Co-Chair of the INNS Big Data section and the annual INNS Conference on Big Data, and Chapter Chair of the IEEE UK and RI’s Industry Applications Society. He is a Fellow of the UK’s Higher Education Academy and Senior Fellow of the Brain Sciences Foundation.